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Stronger Synaptic Connectivity As a Mechanism Behind Development of Working Memory–Related Brain Activity During Childhood

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Stronger Synaptic Connectivity As a Mechanism Behind Development of Working Memory–Related Brain Activity During Childhood Stronger Synaptic Connectivity as a Mechanism behind Development of Working Memory–related Brain Activity during Childhood Fredrik Edin1,2, Julian Macoveanu1, Pernille Olesen1, Jesper Tegne´r1,3,4, and Torkel Klingberg1 Downloaded from http://mitprc.silverchair.com/jocn/article-pdf/19/5/750/1756680/jocn.2007.19.5.750.pdf by guest on 18 May 2021 Abstract & The cellular maturational processes behind cognitive de- compared to brain activity measured with functional magnetic velopment during childhood, including the development of resonance imaging in children and adults. We found that net- working memory capacity, are still unknown. By using the works with stronger fronto-parietal synaptic connectivity be- most standard computational model of visuospatial working tween cells coding for similar stimuli, but not those with faster memory, we investigated the consequences of cellular matura- conduction, stronger connectivity within a region, or increased tional processes, including myelination, synaptic strengthen- coding specificity, predict measured developmental increases ing, and synaptic pruning, on working memory-related brain in both working memory-related brain activity and in correla- activity and performance. We implemented five structural de- tions of activity between regions. Stronger fronto-parietal syn- velopmental changes occurring as a result of the cellular matu- aptic connectivity between cells coding for similar stimuli was rational processes in the biophysically based computational thus the only developmental process that accounted for the network model. The developmental changes in memory activ- observed changes in brain activity associated with develop- ity predicted from the simulations of the model were then ment of working memory during childhood. & INTRODUCTION Klingberg, Forssberg, & Westerberg, 2002; Casey, Giedd, & Working memory (WM), the ability to temporarily main- Thomas, 2000; Giedd et al., 1999; Sowell, Thompson, tain visuospatial information in mind, is a key cognitive Holmes, Jernigan, & Toga, 1999). Despite the fact that function that underlies other cognitive abilities such as interpretations of the developmental changes in brain complex reasoning, and undergoes significant maturation activity have been made by referring to structural matura- during childhood and adolescence (Gathercole, Pickering, tional processes (Bunge et al., 2002; Casey et al., 2000), Ambridge, & Wearing, 2004; Westerberg, Hirvikoski, it has actually not yet been demonstrated whether the Forssberg, & Klingberg, 2004; Fry & Hale, 2000; Baddeley maturational changes occurring at the cellular level really & Hitch, 1974). Several maturational processes take place result in the gross changes in brain activity associated during that time, most importantly, the myelination of with cognitive development, nor has it been demonstrated axons, the strengthening of synapses, and synaptic prun- how this process may occur. For example, how would ing (Bourgeois, Goldman-Rakic, & Rakic, 2000; Lamantia myelination, which increases signal conduction velocity, & Rakic, 1990; Rakic, Bourgeois, Eckenhoff, Zecevic, affect macroscopic brain activity as measured with fMRI? & Goldman-Rakic, 1986; Huttenlocher, 1979; Yakovlev & Does synaptic pruning cause increases or decreases in Lecours, 1967; Hubel & Wiesel, 1963). Anatomical and brain activity? Here, by integrating computational model- functional magnetic resonance imaging (fMRI) has ing, with which we can predict the effects of structural been used to map structural and physiological changes changes on brain activity, and fMRI, with which we can associated with cognitive development during child- learn which of the predicted changes in brain activity ac- hood (Olesen, Nagy, Westerberg, & Klingberg, 2003; tually occur, we make the connection between structural Bunge, Dudukovic, Thomason, Vaidya, & Gabrieli, 2002; changes and physiological development during the matu- ration of visuospatial WM (vsWM) in the adolescent. The computational model that we use to make pre- 1Karolinska Institutet, Stockholm, Sweden, 2Royal Institute of dictions about the development of macroscopic brain Technology, Stockholm, Sweden, 3Stockholm Bioinformatics activity underlying vsWM relies on knowledge about the Center, Stockholm, Sweden, 4Linko¨ping University of Tech- neuronal basis of vsWM. Specifically, electrophysiologi- nology, Sweden cal experiments on behaving monkeys reveal sustained D 2007 Massachusetts Institute of Technology Journal of Cognitive Neuroscience 19:5, pp. 750–760 Downloaded from http://www.mitpressjournals.org/doi/pdf/10.1162/jocn.2007.19.5.750 by guest on 30 September 2021 neuronal activity during the delay period of vsWM trials were put forth, each describing in detail a single aspect (Funahashi, Bruce, & Goldman-Rakic, 1989). The activity of structural development. Thus, the effect of each struc- is cue-specific so that different neurons code for objects tural change could be studied in isolation. The develop- at different angles in the visual field. In parallel with the mental change of each hypothesis could be modeled by characterization of vsWM-related activity on the neuro- a simple change of a single parameter in the network nal level, progress in basic cortical physiology has pro- model. For each hypothesis, we produced a ‘‘child’’ ver- duced a detailed description of cellular and synaptic sion and an ‘‘adult’’ version of the network, differing only characteristics of pyramidal cells and inhibitory inter- in the parameter change relating to that specific hypothe- neurons (Douglas & Martin, 2004). These findings were sis. The ‘‘adult’’ version of the network was common to Downloaded from http://mitprc.silverchair.com/jocn/article-pdf/19/5/750/1756680/jocn.2007.19.5.750.pdf by guest on 18 May 2021 recently incorporated in a biophysically based computa- all hypotheses. Simulations were performed with each of tional network model of vsWM activity (Wang, Tegne´r, the networks, and the difference between characteristics Constantinidis, & Goldman-Rakic, 2004; Tegne´r, Compte, of the simulated BOLD activity in the ‘‘child’’ and ‘‘adult’’ & Wang, 2002; Wang, 2001; Compte, Brunel, Goldman- networks of each hypothesis was the prediction from Rakic, & Wang, 2000; Amit & Brunel, 1997), from which that hypothesis. In order to test the prediction of the the model in this study was developed. The model has model, we let a group of children and a group of adults been able to explain several characteristics of the activ- perform a vsWM task while the delay-phase BOLD activity ity in the frontal cortex (Funahashi et al., 1989) during was measured. In this way, we could conclude which of the performance of a WM task, as reviewed by Compte the hypotheses could accurately predict experimentally (2006). It reproduces a low but stable activity during measured developmental changes in brain activity relat- the fixation period, as well as a higher and stable mem- ing to vsWM and which could not. ory activity with physiological firing rates during the delay period. It also explains the decrease in delay- period activity in cells not coding for the presented METHODS visual stimulus. Furthermore, it has predicted differ- ential connection strength between neurons depend- To make the logical structure of the study easier to ing on the degree of similarity of the stimuli that they grasp, the more technical parts concerning modeling encode. This has later been confirmed in experiments and fMRI data acquisition have been moved to the (Constantinidis, Franowicz, & Goldman-Rakic, 2001), Supplemental Methods section. indicating some predictive power of the model. Unfortunately, the original version of the model only Computational Model: General Overview describes vsWM activity in one region of the frontal cortex, whereas vsWM studies in humans (Curtis, Rao, The structure of the vsWM network model that we creat- & D’Esposito, 2004; Rowe, Toni, Josephs, Frackowiak, & ed is shown in Figure 1A and B. The network contains Passingham, 2000; Courtney, Petit, Maisog, Ungerleider, two interconnected regions, each consisting of a popu- & Haxby, 1998) and monkeys (Chafee & Goldman- lation of 128 pyramidal cells (P) and a population of Rakic, 1998, 2000) have found sustained delay activity 32 inhibitory interneurons (I). Every cell codes for an associated with vsWM in several regions, most consist- angle in the visual field. The two regions are replicas of ently in the superior frontal sulcus (SFS, presumably the frontal region network in Tegne´r et al. (2002), and monkey area 8a) and the intraparietal sulcus (IPS, like that model, this model also consists of Hodgkin– presumably monkey area 7ip). Therefore, in order to Huxley type cells with ion channels and input–output evaluate hypotheses about which neuronal developmen- relations matching those of real layer II/III neurons. The tal process accounts for the developmental improve- regions are connected only through their pyramidal ment in vsWM, we extended the single-region vsWM cells. Interregional connections have a conduction delay computational network model to a two-region model. (Ferraina, Pare, & Wurtz, 2002), whereas all other con- This allowed us to investigate hypotheses concerning nections are instantaneous. There exists a topography in strengthening of synapses, both within a region and be- the connection strength between pyramidal cells within tween regions, as well as synaptic pruning and the ve- or between two regions, as indicated by the connection locity of
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